# importar librerias necesarias para analisis de datos y visualizacion

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# cargar datos con separacion ;
data_2021 = pd.read_csv("INFE_Spain_csv_2021.csv", sep=";")
data_2021

# convertir columna id en indice
data_2021 = data_2021.set_index("ID")
data_2021.head()

# Mostrar estadísticas descriptivas de las columnas numéricas
print(data_2021.describe())

# Verificar tipos de datos y valores faltantes
print(data_2021.info())

               wght          QD1          QD2        QD5_1        QD5_2  \
count   7764.000000  7764.000000  7764.000000  7764.000000  7764.000000   
mean    4639.768677     0.518805     8.822257     0.123776     0.621716   
std     2990.281480     0.499678     4.787225     0.329348     0.484990   
min      420.189910     0.000000     1.000000     0.000000     0.000000   
25%     2446.758500     0.000000     5.000000     0.000000     0.000000   
50%     3852.094450     1.000000     9.000000     0.000000     1.000000   
75%     6402.205600     1.000000    13.000000     0.000000     1.000000   
max    19411.416000     1.000000    17.000000     1.000000     1.000000   

             QD5_3        QD5_4        QD5_5        QD5_6        QD5_7  ...  \
count  7764.000000  7764.000000  7764.000000  7764.000000  7764.000000  ...   
mean      0.269835     0.209557     0.231195     0.002061     0.004894  ...   
std       0.443903     0.407019     0.421624     0.045352     0.069793  ...   
min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   
25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   
50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   
75%       1.000000     0.000000     0.000000     0.000000     0.000000  ...   
max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   

            QD11_2       QD11_4       QD11_5       QD11_6       QD11_7  \
count  7764.000000  7764.000000  7764.000000  7764.000000  7764.000000   
mean      0.003349     0.068650    -0.011077    -0.019964    -0.020350   
std       1.161111     1.186923     1.154833     1.150859     1.150684   
min     -99.000000   -99.000000   -99.000000   -99.000000   -99.000000   
25%       0.000000     0.000000     0.000000     0.000000     0.000000   
50%       0.000000     0.000000     0.000000     0.000000     0.000000   
75%       0.000000     0.000000     0.000000     0.000000     0.000000   
max       1.000000     1.000000     1.000000     1.000000     1.000000   

            QD11_8       QD11_9      QD11_10      QD11_99         QD12  
count  7764.000000  7764.000000  7764.000000  7764.000000  7764.000000  
mean     -0.020994     0.014297    -0.021638     0.000129     0.878928  
std       1.150393     1.165734     1.150101     0.011349     0.326231  
min     -99.000000   -99.000000   -99.000000     0.000000     0.000000  
25%       0.000000     0.000000     0.000000     0.000000     1.000000  
50%       0.000000     0.000000     0.000000     0.000000     1.000000  
75%       0.000000     0.000000     0.000000     0.000000     1.000000  
max       1.000000     1.000000     1.000000     1.000000     1.000000  

[8 rows x 189 columns]
<class 'pandas.core.frame.DataFrame'>
Index: 7764 entries, 1 to 7764
Columns: 193 entries, wght to QD13
dtypes: float64(47), int64(142), object(4)
memory usage: 11.5+ MB
None

# mostrar los tipos de datos de las columnas
data_2021.dtypes

wght       float64
QD1          int64
QD2          int64
QD3         object
QD4         object
            ...   
QD11_9       int64
QD11_10      int64
QD11_99      int64
QD12         int64
QD13        object
Length: 193, dtype: object

# cambiar valores de columna QD13 para que sean mas cortos
data_2021["QD13"] = data_2021["QD13"].replace({'Between 15000 and 47000 euro': '15000 - 47000 €',
                                               'Below 15000 euro -bottom 25': 'Below 15000€',
                                               'Above 47000 euro -top 25' : 'Above 47000 €',
                                               'Missing because of interviewer or CAPI error': 'Interviewer or CAPI error'
                                               })
data_2021["QD13"].value_counts()

QD13
15000 - 47000 €              4060
Below 15000€                 1712
Above 47000 €                1510
Do not know                   240
Refuse to answer              222
Interviewer or CAPI error      20
Name: count, dtype: int64

def classify_age_band(age):
    if 18 <= age <= 19:
        return 'teenager'
    elif 20 <= age <= 29:
        return '20s'
    elif 30 <= age <= 39:
        return '30s'
    elif 40 <= age <= 49:
        return '40s'
    elif 50 <= age <= 59:
        return '50s'
    elif 60 <= age <= 69:
        return '60s'
    elif 70 <= age <= 79:
        return '70s'
    elif age >= 80:
        return '80+'
    else:
        return -99  # Para cualquier edad no contemplada o datos faltantes

# Aplicar la función para crear la nueva columna 'QD7_a'
data_2021['QD7_a'] = data_2021['QD7'].apply(classify_age_band)

# Verificar los resultados
print(data_2021[['QD7', 'QD7_a']].head())

    QD7 QD7_a
ID           
1    56   50s
2    34   30s
3    39   30s
4    40   40s
5    46   40s

data_2021['QD7'].describe(include='all')

count    7764.000000
mean       46.959299
std        15.649587
min        18.000000
25%        35.000000
50%        47.000000
75%        59.000000
max        80.000000
Name: QD7, dtype: float64

# histograma con la distribución de las edades (columna QD7)
plt.figure(figsize=(10, 6))
sns.histplot(data = data_2021, x="QD7", bins=20, kde=True)
plt.title("Distribución de edades")
plt.xlabel("Edad")
plt.ylabel("Frecuencia")
plt.show()

# Generando una tabla con la cantidad de valores missing para cada columna y su porcentje
missing_values = data_2021.isnull().sum()
missing_values_percent = 100 * data_2021.isnull().sum() / len(data_2021)
missing_values_table = pd.concat([missing_values, missing_values_percent], axis=1)
missing_values_table = missing_values_table.rename(columns={0: "Missing Values", 1: "%"})
missing_values_table = missing_values_table[missing_values_table.iloc[:, 1] != 0].sort_values("%", ascending=False).round(1)
print("Hay " + str(missing_values_table.shape[0]) + " columnas con valores faltantes")
missing_values_table

Hay 43 columnas con valores faltantes

#eliminar todas las columnas con valores nulos ya que el 100% de los datos son nulos
data_2021 = data_2021.dropna(axis=1)
data_2021

# mostrar columnas con valores missing
data_2021.columns[data_2021.isnull().sum() > 0]

Index([], dtype='object')

# mostrar columnas con valores tipo object
data_2021.dtypes[data_2021.dtypes == "object"]

QD3      object
QD4      object
QP4      object
QD13     object
QD7_a    object
dtype: object

# mostrar valores de la columna QD3
data_2021["QD3"].value_counts()

QD3
if equal or above 15000    5361
if below 15000             2403
Name: count, dtype: int64

# label encoding para la columna QD3
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data_2021['QD3_encoded'] = le.fit_transform(data_2021['QD3'])

# elimiar columna QD3
# data_2021 = data_2021.drop("QD3", axis=1)

data_2021['QD3_encoded'].value_counts()

QD3_encoded
1    5361
0    2403
Name: count, dtype: int64

# mostrar valores columna QD4
print(data_2021["QD4"].value_counts())

QD4
Spanish    7764
Name: count, dtype: int64

# ELIMINAR COLUMNA QD4
data_2021 = data_2021.drop(columns="QD4")

# mostrar valores columna QD13
print(data_2021["QD13"].value_counts())

QD13
15000 - 47000 €              4060
Below 15000€                 1712
Above 47000 €                1510
Do not know                   240
Refuse to answer              222
Interviewer or CAPI error      20
Name: count, dtype: int64

data_encoded_2021 = pd.get_dummies(data_2021, columns=['QD13'], prefix='Income')
print(data_encoded_2021.head())

         wght  QD1  QD2                      QD3  QD5_1  QD5_2  QD5_3  QD5_4  \
ID                                                                             
1   2594.2485    0    8  if equal or above 15000      0      0      0      1   
2   1825.7745    1   11           if below 15000      0      1      1      0   
3   3207.1128    1   14  if equal or above 15000      0      1      1      0   
4   3009.3894    0    8           if below 15000      0      1      1      0   
5   5779.9355    1    9  if equal or above 15000      0      1      0      1   

    QD5_5  QD5_6  ...  QD11_99  QD12  QD7_a  QD3_encoded  \
ID                ...                                      
1       0      0  ...        0     1    50s            1   
2       0      0  ...        0     1    30s            0   
3       0      0  ...        0     0    30s            1   
4       0      0  ...        0     1    40s            0   
5       0      0  ...        0     0    40s            1   

    Income_15000 - 47000 €  Income_Above 47000 €  Income_Below 15000€  \
ID                                                                      
1                     True                 False                False   
2                     True                 False                False   
3                    False                 False                 True   
4                    False                  True                False   
5                     True                 False                False   

    Income_Do not know  Income_Interviewer or CAPI error  \
ID                                                         
1                False                             False   
2                False                             False   
3                False                             False   
4                False                             False   
5                False                             False   

    Income_Refuse to answer  
ID                           
1                     False  
2                     False  
3                     False  
4                     False  
5                     False  

[5 rows x 156 columns]

# Identificar las columnas generadas por pd.get_dummies()
dummy_columns = [col for col in data_encoded_2021 if col.startswith('Income')]

# Convertir sólo esas columnas a uint8 para que salgan como 0 y 1
for column in dummy_columns:
    data_encoded_2021[column] = data_encoded_2021[column].astype('uint8')

print(data_encoded_2021[dummy_columns].head())

    Income_15000 - 47000 €  Income_Above 47000 €  Income_Below 15000€  \
ID                                                                      
1                        1                     0                    0   
2                        1                     0                    0   
3                        0                     0                    1   
4                        0                     1                    0   
5                        1                     0                    0   

    Income_Do not know  Income_Interviewer or CAPI error  \
ID                                                         
1                    0                                 0   
2                    0                                 0   
3                    0                                 0   
4                    0                                 0   
5                    0                                 0   

    Income_Refuse to answer  
ID                           
1                         0  
2                         0  
3                         0  
4                         0  
5                         0

# mostrar valores columna QP4
print(data_encoded_2021["QP4"].value_counts())

QP4
-98                                5930
An unsecured bank loan              377
Insurance                           304
A credit card                       261
An investment account               236
Crypto-assets                       180
Stocks and shares                   164
A mortgage                          161
A savings account                    61
A pension or retirement product      48
0                                    27
Bonds                                 8
-97                                   4
-5                                    2
-99                                   1
Name: count, dtype: int64

# Convertir los códigos a strings para que representen categorías
def label_product(row):
    if row == '-99':
        return 'Refused_to_answer'
    elif row == '-97':
        return 'Dont_know'
    elif row == '0':
        return 'Not_voluntarily_chosen'
    elif row == '-98':
        return 'Not_applicable'
    elif row == '-5':
        return 'Error'
    else:
        return f'{row}'

data_encoded_2021['QP4'] = data_encoded_2021['QP4'].apply(label_product)
print(data_encoded_2021['QP4'].value_counts())

# Aplicar One-Hot Encoding
data_encoded_2021 = pd.get_dummies(data_encoded_2021, columns=['QP4'], prefix='Product')

# Verificamos el resultado
print(data_encoded_2021.filter(regex='Product_').head())

QP4
Not_applicable                     5930
An unsecured bank loan              377
Insurance                           304
A credit card                       261
An investment account               236
Crypto-assets                       180
Stocks and shares                   164
A mortgage                          161
A savings account                    61
A pension or retirement product      48
Not_voluntarily_chosen               27
Bonds                                 8
Dont_know                             4
Error                                 2
Refused_to_answer                     1
Name: count, dtype: int64
    Product_A credit card  Product_A mortgage  \
ID                                              
1                   False               False   
2                   False                True   
3                   False               False   
4                   False               False   
5                   False               False   

    Product_A pension or retirement product  Product_A savings account  \
ID                                                                       
1                                     False                      False   
2                                     False                      False   
3                                     False                      False   
4                                     False                      False   
5                                     False                      False   

    Product_An investment account  Product_An unsecured bank loan  \
ID                                                                  
1                           False                           False   
2                           False                           False   
3                           False                           False   
4                           False                            True   
5                           False                           False   

    Product_Bonds  Product_Crypto-assets  Product_Dont_know  Product_Error  \
ID                                                                           
1           False                  False              False          False   
2           False                  False              False          False   
3           False                  False              False          False   
4           False                  False              False          False   
5           False                  False              False          False   

    Product_Insurance  Product_Not_applicable  Product_Not_voluntarily_chosen  \
ID                                                                              
1               False                    True                           False   
2               False                   False                           False   
3               False                    True                           False   
4               False                   False                           False   
5                True                   False                           False   

    Product_Refused_to_answer  Product_Stocks and shares  
ID                                                        
1                       False                      False  
2                       False                      False  
3                       False                      False  
4                       False                      False  
5                       False                      False

# Identificar las columnas generadas por pd.get_dummies()
dummy_columns = [col for col in data_encoded_2021 if col.startswith('Product')]


# Convertir sólo esas columnas a uint8 para que salgan como 0 y 1
for column in dummy_columns:
    data_encoded_2021[column] = data_encoded_2021[column].astype('uint8')

print(data_encoded_2021[dummy_columns].head())

    Product_A credit card  Product_A mortgage  \
ID                                              
1                       0                   0   
2                       0                   1   
3                       0                   0   
4                       0                   0   
5                       0                   0   

    Product_A pension or retirement product  Product_A savings account  \
ID                                                                       
1                                         0                          0   
2                                         0                          0   
3                                         0                          0   
4                                         0                          0   
5                                         0                          0   

    Product_An investment account  Product_An unsecured bank loan  \
ID                                                                  
1                               0                               0   
2                               0                               0   
3                               0                               0   
4                               0                               1   
5                               0                               0   

    Product_Bonds  Product_Crypto-assets  Product_Dont_know  Product_Error  \
ID                                                                           
1               0                      0                  0              0   
2               0                      0                  0              0   
3               0                      0                  0              0   
4               0                      0                  0              0   
5               0                      0                  0              0   

    Product_Insurance  Product_Not_applicable  Product_Not_voluntarily_chosen  \
ID                                                                              
1                   0                       1                               0   
2                   0                       0                               0   
3                   0                       1                               0   
4                   0                       0                               0   
5                   1                       0                               0   

    Product_Refused_to_answer  Product_Stocks and shares  
ID                                                        
1                           0                          0  
2                           0                          0  
3                           0                          0  
4                           0                          0  
5                           0                          0

#ver las ultimas 30 columnas y todas las filas
data_encoded_2021.iloc[:, -20:]

# eliminar QD3 DE LA TABLA  data_encoded_2021
data_encoded_2021 = data_encoded_2021.drop(columns=['QD7_a'])

#deteccion de outliers en todas las columnas 
import matplotlib.pyplot as plt
import seaborn as sns

# Gráfico boxplot para la columna 'wght'
plt.figure(figsize=(10, 6))
sns.boxplot(data_encoded_2021['wght'])
plt.title('Boxplot de la columna wght')
plt.show()

# Calculando el IQR para la columna 'wght'
Q1 = data_encoded_2021['wght'].quantile(0.25)
Q3 = data_encoded_2021['wght'].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

# Identificar outliers
outliers = data_encoded_2021[(data_encoded_2021['wght'] < lower_bound) | (data_encoded_2021['wght'] > upper_bound)]
print('Número de outliers detectados:', len(outliers))
print('Límite inferior para outliers:', lower_bound)
print('Límite superior para outliers:', upper_bound)

Número de outliers detectados: 140
Límite inferior para outliers: -3486.41215
Límite superior para outliers: 12335.376250000001

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

binary_columns = [  'QF2_1',  'QF2_5', 'QF3_1',	'QF3_2', 'QF3_3', 'QF3_6',	'QF3_7', 'QF3_8', 
                  'QF9_1', 'QF9_2', 'QF9_3', 'QF9_4', 'QF9_5', 'QF9_6', 'QF9_7', 'QF9_8', 'QF9_9', 'QF9_12', 'QF11', 'Income_Below 15000€', 'Income_Above 47000 €', 'Income_15000 - 47000 €'] 
continuous_columns = ['QD7',  'QD2', 'QD5_3', 'QD5_ch', 'QD9_ISCED', 'QD10', 'QK1'] 
# Lista de preguntas de interés


# Filtrar las columnas binarias para incluir solo 0 y 1
filtered_data = data_encoded_2021[binary_columns].apply(lambda x: x.isin([0, 1]))

# Crea un DataFrame que incluya solo las filas válidas para las columnas binarias
cleaned_binary_data = data_encoded_2021.loc[filtered_data.all(axis=1), binary_columns]

# Combina los datos binarios filtrados con los datos continuos
combined_data = pd.concat([cleaned_binary_data, data_encoded_2021[continuous_columns]], axis=1)

# Calcula la matriz de correlación
correlation_matrix = combined_data.corr()

# Visualiza la matriz de correlación
plt.figure(figsize=(30, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Matriz de Correlación para Preguntas Seleccionadas')
plt.show()

import pandas as pd

# Asegúrate de que QD7 y QD9_ISCED están en formato numérico adecuado
data_encoded_2021['QD7'] = pd.to_numeric(data_encoded_2021['QD7'], errors='coerce')  # Convierte a numérico, maneja los errores
data_encoded_2021['QD9_ISCED'] = pd.to_numeric(data_encoded_2021['QD9_ISCED'], errors='coerce')  # Convierte a numérico, maneja los errores

# Calcula la correlación de Pearson
correlation = data_encoded_2021['QD7'].corr(data_encoded_2021['QD9_ISCED'])

print(f"La correlación entre QD7 (Edad) y QD9_ISCED (Nivel de Educación ISCED) es: {correlation}")

La correlación entre QD7 (Edad) y QD9_ISCED (Nivel de Educación ISCED) es: -0.1828594443351182

#BARPLOT QD9_ISCED EN COLORES 
data_EDUCATION_LEVEL = data_encoded_2021['QD9_ISCED'].replace({
    0: 'Early childhood education', 
    1: 'Primary education', 
    2: 'Lower secondary education', 
    3: 'Upper secondary education', 
    4: 'Post-secondary non-tertiary education', 
    5: 'Short-cycle tertiary education', 
    6: 'Bachelor’s or equivalent level', 
    7: 'Master’s or equivalent level', 
    8: 'Doctoral or equivalent level'
})
#BARPLOT DATA_EDUCATION_LEVEL EN COLORES
plt.figure(figsize=(10, 6))
sns.countplot(data = data_EDUCATION_LEVEL, palette= 'viridis')
plt.title('Nivel de educación')
plt.xlabel('Count')
plt.show()

/tmp/ipykernel_2642/1692575795.py:15: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data = data_EDUCATION_LEVEL, palette= 'viridis')

questions = ['QF3_1', 'QF3_2', 'QF3_3', 'QF3_6', 'QF3_7', 'QF3_8']
titulos = ['Saving cash at home or in your wallet',
    'Paying money into a savings/deposit account',
    'Giving money to family to save on your behalf',
    'Investing in crypto-assets or ICOs',
    'Investing in stocks and shares',
    'Saving or investing in some other way, other than a pension']

# # filtrar por sexo femenino
# feminas = data_encoded_2021[data_encoded_2021['QD1'] == 0]

# Set up the matplotlib figure and axes
fig, axs = plt.subplots(1, len(questions), figsize=(25, 6), sharey=True)

for i, question in enumerate(questions):
    # Calculate the percentage of each category within the question
    percent_data = data_encoded_2021[question].value_counts(normalize=True).sort_index() * 100
    # Create a bar plot for each category's percentage
    percent_data.plot(kind='bar', ax=axs[i], color=sns.color_palette('viridis', len(percent_data)))
    axs[i].set_title(titulos[i])
    axs[i].set_xlabel('Categories')
    axs[i].set_ylabel('Percentage')
    # Annotate the percentage on the bars
    for p in axs[i].patches:
        axs[i].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Set a common y-label
fig.text(0.04, 0.5, 'Percentage', va='center', rotation='vertical')

plt.tight_layout()
plt.show()

## barplot de la variable QF8 (sin valores -99, -97 y-5) EN PORCENTAJES
data_retirement = data_encoded_2021[data_encoded_2021['QF8'].isin([1, 2, 3, 4, 5, 6])]
percent_data = data_retirement['QF8'].value_counts(normalize=True).sort_index() * 100
percent_data.plot(kind='bar', color=sns.color_palette('viridis', len(percent_data)))
plt.title('rate from 1 to 5 how well they are preparing their retirement (if not retired yet). If retired, how well did they prepare their retirement.')
plt.xlabel('Categories')
plt.ylabel('Percentage')
plt.xticks([0, 1, 2, 3, 4, 5],['very confident', '2', '3', '4', 'not at all confident', 'no retirement plan'], rotation = 45)
for p in plt.gca().patches:
    plt.gca().annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
plt.show()

questions = ['QF9_1', 'QF9_2', 'QF9_3', 'QF9_4', 'QF9_5', 'QF9_6', 'QF9_7', 'QF9_8', 'QF9_9', 'QF9_12', 'QF9_97', 'QF9_99']
titles_qf9 = [
    'Draw a government pension/old-age benefit',
    'Draw an occupational or workplace pension plan',
    'Draw a private pension plan',
    'Sell financial assets (such as stocks, bonds or mutual funds)',
    'Sell your non-financial assets',
    'From income generated by your financial/non-financial assets',
    'Rely on a spouse/partner income',
    'Rely on your children/other family members to support you',
    'Draw on your savings',
    'Something else',
    'Do not know ',
    'Refused to answer the entire question'
]

# Configura la figura de matplotlib y los ejes
fig, axs = plt.subplots(2, len(questions)//2 + len(questions)%2, figsize=(30, 10), sharey=True)
axs = axs.flatten()  # Aplana el array para facilitar la iteración

for i, question in enumerate(questions):
    # Calcula el porcentaje de cada categoría basado en el total de respuestas
    total_responses = len(data_encoded_2021[question])
    value_counts = data_encoded_2021[question].value_counts().sort_index()
    percentages = (value_counts / total_responses) * 100
    
    # Selecciona solo los porcentajes para 0 y 1 para visualizar en el gráfico
    display_percentages = percentages.loc[[0, 1]]
    
    # Crea un gráfico de barras para las categorías 0 y 1
    sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
    axs[i].set_title(titles_qf9[i])
    axs[i].set_xlabel('Categories')
    axs[i].set_ylabel('Percentage')
    
    # Anota los porcentajes reales en las barras
    for index in display_percentages.index:
        height = display_percentages[index]
        axs[i].annotate(f'{height:.1f}%', (index, height),
                        ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Establece una etiqueta común para el eje Y
fig.text(0.04, 0.5, 'Percentage', va='center', rotation='vertical')

# Elimina los subgráficos vacíos si el número de preguntas no llena exactamente la cuadrícula de subgráficos
for ax in axs[len(questions):]:
    fig.delaxes(ax)

plt.tight_layout()
plt.show()

/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')
/tmp/ipykernel_2642/150885677.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=display_percentages.index, y=display_percentages.values, ax=axs[i], palette='viridis')

# BARPLOT variable QF11 (cambia -99 por "Refused to answer", -98 por "Not applicable", -97 por "Dont know", 0 por "No", 1 por "Yes")
data_income_expenses = data_encoded_2021['QF11'].replace({
    -99: 'Refused ',
    -5: 'Programming error',
    -97: 'Dont know',
    0: 'No',
    1: 'Yes'
})
percent_data = data_income_expenses.value_counts(normalize=True) * 100
percent_data.plot(kind='bar', color=sns.color_palette('viridis', len(percent_data)))
plt.title('Sometimes people find that their income does not quite cover their living expenses. In the last 12 months, has this happened to you, personally?')
plt.ylabel('Percentage')
plt.xticks(rotation=0)
for p in plt.gca().patches:
    plt.gca().annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
plt.show()

# filtra por los que respondieron 1 a QF11
expenses_higher_income = data_2021[data_2021['QF11'] == 1]
print(expenses_higher_income['QF11'].value_counts())

# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.countplot(x= 'QD13', data=expenses_higher_income, ax=axes[0])
sns.countplot(x= 'QD9_ISCED', data=expenses_higher_income, ax=axes[1])
sns.histplot(x= 'QD7', data=expenses_higher_income, ax=axes[2])
sns.countplot(x= 'QD2', data=expenses_higher_income, ax=axes[3])
axes[0].set_title('Distribución de ingresos')
axes[1].set_title('Nivel de Educación')
axes[2].set_title('Distribución de Edades')
axes[3].set_title('Distribución de Regiones')
plt.tight_layout()
plt.show()

QF11
1    1752
Name: count, dtype: int64

# COuntplot de qd2
plt.figure(figsize=(10, 6))
sns.countplot(x='QD2', data=data_2021, palette='viridis')
plt.title('Distribución de Regiones')
plt.xlabel('Regiones')
plt.ylabel('Count')
plt.show()

/tmp/ipykernel_2642/1584208492.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='QD2', data=data_2021, palette='viridis')

data_2021['QD13'].value_counts()

QD13
15000 - 47000 €              4060
Below 15000€                 1712
Above 47000 €                1510
Do not know                   240
Refuse to answer              222
Interviewer or CAPI error      20
Name: count, dtype: int64

# ver ultimas 40 columnas de data_encoded_2021
data_encoded_2021.iloc[:, -25:]

# 1

# Calcula el total de respuestas y las respuestas "Sí" por región
total_por_region = data_2021['QD2'].value_counts()
si_por_region = data_2021[data_2021['QF11'] == 1]['QD2'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada región
proporcion_si_por_region = (si_por_region / total_por_region) * 100

# 2

# Calcula el total de respuestas y las respuestas "Sí" por nivel de educación
total_por_educacion = data_2021['QD9_ISCED'].value_counts()
si_por_educacion = data_2021[data_2021['QF11'] == 1]['QD9_ISCED'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada nivel de educación
proporcion_si_por_educacion = (si_por_educacion / total_por_educacion) * 100

# 3

# Calcula el total de respuestas y las respuestas "Sí" por nivel de ingresos
total_por_ingresos = data_2021['QD13'].value_counts()
si_por_ingresos = data_2021[data_2021['QF11'] == 1]['QD13'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada nivel de ingresos
proporcion_si_por_ingresos = (si_por_ingresos / total_por_ingresos) * 100

# Calcula el total de respuestas y las respuestas "Sí" por municipality size (QD3)
total_por_municipio = data_2021['QD3'].value_counts()
si_por_municipio = data_2021[data_2021['QF11'] == 1]['QD3'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada tamaño de municipio
proporcion_si_por_municipio = (si_por_municipio / total_por_municipio) * 100

# Configura la figura y los ejes
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(25, 8))  # Ajusta el tamaño según sea necesario

# Gráfico 1: Porcentaje de Respuestas "Sí" a QF11 por Región
sns.barplot(x=proporcion_si_por_region.index, y=proporcion_si_por_region, ax=axes[0], palette= 'muted')
axes[0].set_title('Porcentaje de Respuestas "Sí" a QF11 por Región')
axes[0].set_xlabel('Región')
axes[0].set_ylabel('Porcentaje')
axes[0].set_xticklabels(['Andalucía', 'Aragón', 'Asturias', 'Baleares', 'Canarias', 'Cantabria', 'Castilla y León', 'Castilla-La Mancha', 'Cataluña', 'Valencia', 'Extremadura', 'Galicia', 'Madrid', 'Murcia', 'Navarra', 'País Vasco', 'La Rioja'], rotation=60)
for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Gráfico 2: Porcentaje de Respuestas "Sí" a QF11 por Nivel de Educación
sns.barplot(x=proporcion_si_por_educacion.index, y=proporcion_si_por_educacion, ax=axes[1], palette= 'muted')
axes[1].set_title('Porcentaje de Respuestas "Sí" a QF11 por Nivel de Educación')
axes[1].set_xlabel('Nivel de Educación ISCED')
axes[1].set_ylabel('Porcentaje')
axes[1].set_xticklabels(['Early childhood education', 'Primary education', 'Lower secondary education', 'Upper secondary education', 'Post-secondary non-tertiary education', 'Short-cycle tertiary education', 'Bachelor’s or equivalent level', 'Master’s or equivalent level', 'Doctoral or equivalent level'], rotation=60)
for p in axes[1].patches:
    axes[1].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Gráfico 3: Porcentaje de Respuestas "Sí" a QF11 por Nivel de Ingresos
sns.barplot(x=proporcion_si_por_ingresos.index, y=proporcion_si_por_ingresos, ax=axes[2], palette= 'muted')
axes[2].set_title('Porcentaje de Respuestas "Sí" a QF11 por Nivel de Ingresos')
axes[2].set_xlabel('Nivel de Ingresos')
axes[2].set_ylabel('Porcentaje')
for p in axes[2].patches:
    axes[2].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

plt.tight_layout() 
plt.show()

/tmp/ipykernel_2642/1998170681.py:5: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=proporcion_si_por_region.index, y=proporcion_si_por_region, ax=axes[0], palette= 'muted')
/tmp/ipykernel_2642/1998170681.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axes[0].set_xticklabels(['Andalucía', 'Aragón', 'Asturias', 'Baleares', 'Canarias', 'Cantabria', 'Castilla y León', 'Castilla-La Mancha', 'Cataluña', 'Valencia', 'Extremadura', 'Galicia', 'Madrid', 'Murcia', 'Navarra', 'País Vasco', 'La Rioja'], rotation=60)
/tmp/ipykernel_2642/1998170681.py:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=proporcion_si_por_educacion.index, y=proporcion_si_por_educacion, ax=axes[1], palette= 'muted')
/tmp/ipykernel_2642/1998170681.py:18: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axes[1].set_xticklabels(['Early childhood education', 'Primary education', 'Lower secondary education', 'Upper secondary education', 'Post-secondary non-tertiary education', 'Short-cycle tertiary education', 'Bachelor’s or equivalent level', 'Master’s or equivalent level', 'Doctoral or equivalent level'], rotation=60)
/tmp/ipykernel_2642/1998170681.py:23: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=proporcion_si_por_ingresos.index, y=proporcion_si_por_ingresos, ax=axes[2], palette= 'muted')

data_2021[data_2021['QP4'] != '-98'].value_counts('QP4',normalize = True) *100

QP4
An unsecured bank loan             20.556161
Insurance                          16.575791
A credit card                      14.231189
An investment account              12.868048
Crypto-assets                       9.814613
Stocks and shares                   8.942203
A mortgage                          8.778626
A savings account                   3.326063
A pension or retirement product     2.617230
0                                   1.472192
Bonds                               0.436205
-97                                 0.218103
-5                                  0.109051
-99                                 0.054526
Name: proportion, dtype: float64

#filtrar valores QP4. todos menos -98
barplot_qp4 = data_2021[data_2021['QP4'] != '-98'].value_counts('QP4',normalize = True) *100

# VISUALIZAR EN UN BARPLOT
plt.figure(figsize=(20, 6))
sns.barplot(x=barplot_qp4.index, y=barplot_qp4, palette= 'viridis')
plt.title('Most recent product acquired')
plt.xlabel('QP4')
plt.ylabel('Percentage')
for p in plt.gca().patches:
    plt.gca().annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
plt.xticks(rotation = 45)
plt.tight_layout()
plt.show()

/tmp/ipykernel_2642/2697532845.py:6: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=barplot_qp4.index, y=barplot_qp4, palette= 'viridis')

data_encoded_2021['QP5'].value_counts()

QP5
-98    4226
 1     1360
 3     1080
 2      908
 4      140
-97      30
-5       14
-99       6
Name: count, dtype: int64

# filtra por los que respondieron 1 a Qs1_8
expenses_higher_income = data_2021[data_2021['QS1_8'] == 1]
print(expenses_higher_income['QS1_8'].value_counts())

# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.countplot(x= 'QD13', data=expenses_higher_income, ax=axes[0])
sns.countplot(x= 'QD9_ISCED', data=expenses_higher_income, ax=axes[1])
sns.histplot(x= 'QD7', data=expenses_higher_income, ax=axes[2])
sns.countplot(x= 'QD2', data=expenses_higher_income, ax=axes[3])
axes[0].set_title('Distribución de ingresos')
axes[1].set_title('Nivel de Educación')
axes[2].set_title('Distribución de Edades')
axes[3].set_title('Distribución de Regiones')
plt.tight_layout()
plt.show()

QS1_8
1    1387
Name: count, dtype: int64

# distribucion respuestas pregunta QS1_4
plt.figure(figsize=(10, 6))
sns.countplot(x='QS1_4', data=data_2021, palette='viridis')
plt.title('Distribución de respuestas a QS1_4')
plt.xlabel('QS1_4')
plt.ylabel('Count')
plt.show()

/tmp/ipykernel_2642/4014492808.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='QS1_4', data=data_2021, palette='viridis')

# filtra por los que respondieron 1 a Qs1_8
expenses_higher_income = data_2021[data_2021['QS2_5'] == 1]
print(expenses_higher_income['QS2_5'].value_counts())

# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.countplot(x= 'QD13', data=expenses_higher_income, ax=axes[0])
sns.countplot(x= 'QD9_ISCED', data=expenses_higher_income, ax=axes[1])
sns.histplot(x= 'QD7', data=expenses_higher_income, ax=axes[2])
sns.countplot(x= 'QD2', data=expenses_higher_income, ax=axes[3])
axes[0].set_title('Distribución de ingresos')
axes[1].set_title('Nivel de Educación')
axes[2].set_title('Distribución de Edades')
axes[3].set_title('Distribución de Regiones')
plt.tight_layout()
plt.show()

QS2_5
1    6663
Name: count, dtype: int64

# distribucion respuestas pregunta QS3_9
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 6))
sns.countplot(x= 'QS3_9', data=data_2021, ax=axes[0])
sns.countplot(x= 'QS3_11', data=data_2021, ax=axes[1])
axes[0].set_title('I am concerned that my money won’t last ')
axes[1].set_title('I tend to live for today and let tomorrow take care of itself')
axes[0].set_xticks([0, 1, 2, 3, 4, 5, 6],['refused', 'don’t know', '1=completely', '2', '3', '4', '5=not at all'], rotation = 45)
axes[1].set_xticks([0, 1, 2, 3, 4, 5, 6],['refused', 'don’t know', '1=completely', '2', '3', '4', '5=not at all'], rotation = 45)
plt.tight_layout()
plt.show()

#PORCENTAJES QD12 BARPLOT 
a = data_encoded_2021['QD12'].value_counts(normalize= True) *100 
plt.figure(figsize=(10, 6))
sns.barplot(x=a.index, y=a, palette= 'viridis')
plt.title('Where you born in Spain?')
plt.xticks([0,1],['No', 'Yes'])
for p in plt.gca().patches:
    plt.gca().annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
plt.show()

/tmp/ipykernel_2642/3033627799.py:4: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=a.index, y=a, palette= 'viridis')

# distribucion respuestas pregunta QK1
qk1 = data_encoded_2021['QK1'].value_counts(normalize= True) *100 
plt.figure(figsize=(10, 6))
sns.barplot(x=qk1.index, y=qk1, palette= 'viridis')
plt.title('how you would rate your overall knowledge about financial matters compared with other adults in Spain')
plt.xticks([0,1,2,3,4,5,6],['dont know', 'refused', 'very high', 'quite high', 'about average', 'quite low', 'very low'])
for p in plt.gca().patches:
    plt.gca().annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
plt.show()

/tmp/ipykernel_2642/3491748961.py:4: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=qk1.index, y=qk1, palette= 'viridis')

# filtra por los que respondieron 1 a Qs1_8
expenses_higher_income = data_2021[data_2021['QK1'] == 2]
print(expenses_higher_income['QK1'].value_counts())

# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.countplot(x= 'QD13', data=expenses_higher_income, ax=axes[0])
sns.countplot(x= 'QD9_ISCED', data=expenses_higher_income, ax=axes[1])
sns.histplot(x= 'QD7', data=expenses_higher_income, ax=axes[2])
sns.countplot(x= 'QD2', data=expenses_higher_income, ax=axes[3])
axes[0].set_title('Distribución de ingresos')
axes[1].set_title('Nivel de Educación')
axes[2].set_title('Distribución de Edades')
axes[3].set_title('Distribución de Regiones')
plt.tight_layout()
plt.show()

QK1
2    598
Name: count, dtype: int64

# 1

# Calcula el total de respuestas y las respuestas "Sí" por región
total_por_region = data_2021['QD2'].value_counts()
si_por_region = data_2021[data_2021['QK1'] == 5]['QD2'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada región
proporcion_si_por_region = (si_por_region / total_por_region) * 100

# 2

# Calcula el total de respuestas y las respuestas "Sí" por nivel de educación
total_por_educacion = data_2021['QD9_ISCED'].value_counts()
si_por_educacion = data_2021[data_2021['QK1'] == 5]['QD9_ISCED'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada nivel de educación
proporcion_si_por_educacion = (si_por_educacion / total_por_educacion) * 100

# 3

# Calcula el total de respuestas y las respuestas "Sí" por nivel de ingresos
total_por_ingresos = data_2021['QD13'].value_counts()
si_por_ingresos = data_2021[data_2021['QK1'] == 5]['QD13'].value_counts()

# Calcula la proporción de respuestas "Sí" en cada nivel de ingresos
proporcion_si_por_ingresos = (si_por_ingresos / total_por_ingresos) * 100

# Configura la figura y los ejes
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(25, 8))  # Ajusta el tamaño según sea necesario

# Gráfico 1: Porcentaje de Respuestas "Sí" a QK1 por Región
sns.barplot(x=proporcion_si_por_region.index, y=proporcion_si_por_region, ax=axes[0], palette= 'muted')
axes[0].set_title('Porcentaje de Respuestas "VERY LOW" a QK1 por Región')
axes[0].set_xlabel('Región')
axes[0].set_ylabel('Porcentaje')
axes[0].set_xticklabels(['Andalucía', 'Aragón', 'Asturias', 'Baleares', 'Canarias', 'Cantabria', 'Castilla y León', 'Castilla-La Mancha', 'Cataluña', 'Valencia', 'Extremadura', 'Galicia', 'Madrid', 'Murcia', 'Navarra', 'País Vasco', 'La Rioja'], rotation=60)
for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Gráfico 2: Porcentaje de Respuestas "Sí" a QF11 por Nivel de Educación
sns.barplot(x=proporcion_si_por_educacion.index, y=proporcion_si_por_educacion, ax=axes[1], palette= 'muted')
axes[1].set_title('Porcentaje de Respuestas "VERY LOW" a QK1 por Nivel de Educación')
axes[1].set_xlabel('Nivel de Educación ISCED')
axes[1].set_ylabel('Porcentaje')
axes[1].set_xticklabels(['Early childhood education', 'Primary education', 'Lower secondary education', 'Upper secondary education', 'Post-secondary non-tertiary education', 'Short-cycle tertiary education', 'Bachelor’s or equivalent level', 'Master’s or equivalent level', 'Doctoral or equivalent level'], rotation=60)
for p in axes[1].patches:
    axes[1].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Gráfico 3: Porcentaje de Respuestas "Sí" a QF11 por Nivel de Ingresos
sns.barplot(x=proporcion_si_por_ingresos.index, y=proporcion_si_por_ingresos, ax=axes[2], palette= 'muted')
axes[2].set_title('Porcentaje de Respuestas "VERY LOW" a QK1 por Nivel de Ingresos')
axes[2].set_xlabel('Nivel de Ingresos')
axes[2].set_ylabel('Porcentaje')
for p in axes[2].patches:
    axes[2].annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

plt.tight_layout() 
plt.show()

/tmp/ipykernel_2642/4269437081.py:5: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=proporcion_si_por_region.index, y=proporcion_si_por_region, ax=axes[0], palette= 'muted')
/tmp/ipykernel_2642/4269437081.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axes[0].set_xticklabels(['Andalucía', 'Aragón', 'Asturias', 'Baleares', 'Canarias', 'Cantabria', 'Castilla y León', 'Castilla-La Mancha', 'Cataluña', 'Valencia', 'Extremadura', 'Galicia', 'Madrid', 'Murcia', 'Navarra', 'País Vasco', 'La Rioja'], rotation=60)
/tmp/ipykernel_2642/4269437081.py:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=proporcion_si_por_educacion.index, y=proporcion_si_por_educacion, ax=axes[1], palette= 'muted')
/tmp/ipykernel_2642/4269437081.py:18: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axes[1].set_xticklabels(['Early childhood education', 'Primary education', 'Lower secondary education', 'Upper secondary education', 'Post-secondary non-tertiary education', 'Short-cycle tertiary education', 'Bachelor’s or equivalent level', 'Master’s or equivalent level', 'Doctoral or equivalent level'], rotation=60)
/tmp/ipykernel_2642/4269437081.py:23: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=proporcion_si_por_ingresos.index, y=proporcion_si_por_ingresos, ax=axes[2], palette= 'muted')

# FILTRAR DATASET POR RESPUESTAS A QK2 DISTINTAS DE 200 (respuesta incorrecta)
data_QK2 = data_2021[data_2021['QK2'] != 200]

# 
# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.countplot(x= 'QD13', data=data_QK2, ax=axes[0])
sns.countplot(x= 'QD9_ISCED', data=data_QK2, ax=axes[1])
sns.histplot(x= 'QD7', data=data_QK2, ax=axes[2])
sns.countplot(x= 'QD2', data=data_QK2, ax=axes[3])
axes[0].set_title('Distribución de ingresos')
axes[1].set_title('Nivel de Educación')
axes[2].set_title('Distribución de Edades')
axes[3].set_title('Distribución de Regiones')
for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():.1f}', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
plt.tight_layout()
plt.show()

# Definir las respuestas correctas y las múltiples respuestas correctas donde sea necesario
respuestas_correctas = {
    'QK4': 0,  # Asumiendo que '0' es la respuesta correcta para QK4 (sin interés)
    'QK5': 102, # Asumiendo que '102' es la respuesta correcta para QK5 (100 principal + 2% de interés)
    'QK7_1': True,  # Asumiendo que True es la respuesta correcta para QK7_1
    'QK7_2': True,
    'QK7_3': True
}

# Respuestas múltiples para QK3
data_2021['QK3_puntos'] = data_2021['QK3'].apply(lambda x: 1 if x in [3, 4] else 0)

# Calcular puntos por cada respuesta correcta, excepto QK6
for pregunta, correcta in respuestas_correctas.items():
    data_2021[pregunta + '_puntos'] = (data_2021[pregunta] == correcta).astype(int)

# Asignar puntos para QK6 condicionado a QK5 siendo correcto también
data_2021['QK6_puntos'] = ((data_2021['QK6'] == 1) & (data_2021['QK5'] == 102)).astype(int)

# Sumar puntos para obtener la nota final de conocimiento financiero
columnas_puntos = [pregunta + '_puntos' for pregunta in respuestas_correctas] + ['QK3_puntos', 'QK6_puntos']
data_2021['Nota_Financial_Knowledge'] = data_2021[columnas_puntos].sum(axis=1)

# Visualizar el resultado
data_2021[['Nota_Financial_Knowledge'] + columnas_puntos].head()

#barplot de la variable Nota_Financial_Knowledge
plt.figure(figsize=(10, 6))
sns.countplot(data = data_2021, x='Nota_Financial_Knowledge', palette= 'viridis')
plt.title('Nota de Conocimiento Financiero')
plt.xlabel('Nota')
plt.ylabel('Count')
plt.show()

print(data_2021['Nota_Financial_Knowledge'].describe())
data_2021['Nota_Financial_Knowledge'].value_counts(normalize=True) * 100

/tmp/ipykernel_2642/3598526257.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data = data_2021, x='Nota_Financial_Knowledge', palette= 'viridis')

count    7764.000000
mean        4.748454
std         1.496861
min         0.000000
25%         4.000000
50%         5.000000
75%         6.000000
max         7.000000
Name: Nota_Financial_Knowledge, dtype: float64

Nota_Financial_Knowledge
4    22.630088
5    21.780010
6    19.448738
3    14.863472
7    14.605873
2     5.358063
1     1.184956
0     0.128800
Name: proportion, dtype: float64

# INGRESOS
total_por_ingresos = data_2021['QD13'].value_counts()
high_knowledge = data_2021[data_2021['Nota_Financial_Knowledge'] > 5]['QD13'].value_counts()

proporcion_por_ingresos = (high_knowledge / total_por_ingresos) * 100

# EDUCACION
high_knowledge1 = data_2021[data_2021['Nota_Financial_Knowledge'] > 5]['QD9_ISCED'].value_counts()
 
proporcion_por_educacion = (high_knowledge1 / total_por_educacion) * 100

# REGION
total_por_region = data_2021['QD2'].value_counts()
high_knowledge2 = data_2021[data_2021['Nota_Financial_Knowledge'] > 5]['QD2'].value_counts()
 
proporcion_por_region = (high_knowledge2 / total_por_region) * 100

# FILTRAR DATASET POR Nota_Financial_Knowledge > 4
high_knowledge3 = data_2021[data_2021['Nota_Financial_Knowledge'] >5]


# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.barplot(x= proporcion_por_ingresos.index, y=proporcion_por_ingresos, ax=axes[0])
sns.barplot(x= proporcion_por_educacion.index, y=proporcion_por_educacion, ax=axes[1])
sns.histplot(x= 'QD7', data=high_knowledge3, ax=axes[3])
sns.barplot(x= proporcion_por_region.index, y=proporcion_por_region, ax=axes[2])
axes[0].set_title('Distribución de ingresos')
axes[0].set_ylabel('Porcentaje')
axes[1].set_title('Nivel de Educación')
axes[1].set_ylabel('Porcentaje')
axes[3].set_title('Distribución de Edades')
axes[2].set_title('Distribución de Regiones')
axes[2].set_ylabel('Porcentaje')

# Anotar los valores en los gráficos
for ax in axes[0:3]:  # Suponiendo que los primeros tres son gráficos de barras
    for p in ax.patches:
        ax.annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha='center', va='center', xytext=(0, 10), textcoords='offset points')


# Ajustar el diseño de los gráficos y mostrarlos
plt.tight_layout()
plt.show()

# INGRESOS
total_por_ingresos = data_2021['QD13'].value_counts()
low_knowledge = data_2021[data_2021['Nota_Financial_Knowledge'] < 4]['QD13'].value_counts()

proporcion_por_ingresos = (low_knowledge / total_por_ingresos) * 100

# EDUCACION
low_knowledge1 = data_2021[data_2021['Nota_Financial_Knowledge'] < 4]['QD9_ISCED'].value_counts()
 
proporcion_por_educacion = (low_knowledge1 / total_por_educacion) * 100

# REGION
total_por_region = data_2021['QD2'].value_counts()
low_knowledge2 = data_2021[data_2021['Nota_Financial_Knowledge'] < 4]['QD2'].value_counts()
 
proporcion_por_region = (low_knowledge2 / total_por_region) * 100

# FILTRAR DATASET POR Nota_Financial_Knowledge < 4
low_knowledge3 = data_2021[data_2021['Nota_Financial_Knowledge'] < 4]


# Análisis demográfico
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(30, 5))
sns.barplot(x= proporcion_por_ingresos.index, y=proporcion_por_ingresos, ax=axes[0])
sns.barplot(x= proporcion_por_educacion.index, y=proporcion_por_educacion, ax=axes[1])
sns.histplot(x= 'QD7', data=low_knowledge3, ax=axes[3])
sns.barplot(x= proporcion_por_region.index, y=proporcion_por_region, ax=axes[2])
axes[0].set_title('Distribución de ingresos')
axes[0].set_ylabel('Porcentaje')
axes[1].set_title('Nivel de Educación')
axes[1].set_ylabel('Porcentaje')
axes[3].set_title('Distribución de Edades')
axes[2].set_title('Distribución de Regiones')
axes[2].set_ylabel('Porcentaje')

# Anotar los valores en los gráficos
for ax in axes[0:3]:  # Suponiendo que los primeros tres son gráficos de barras
    for p in ax.patches:
        ax.annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha='center', va='center', xytext=(0, 10), textcoords='offset points')


# Ajustar el diseño de los gráficos y mostrarlos
plt.tight_layout()
plt.show()

#media de nota_financial_knoledge agrupada por regiones, nivel de ingresos, nivel de educacion y edad
nota_por_region = data_2021.groupby('QD2')['Nota_Financial_Knowledge'].mean()

nota_por_ingresos = data_2021.groupby('QD13')['Nota_Financial_Knowledge'].mean().sort_values(ascending=False)

nota_por_educacion = data_2021.groupby('QD9_ISCED')['Nota_Financial_Knowledge'].mean().sort_values(ascending=False)

nota_por_puesto_trabajo = data_2021.groupby('QD10')['Nota_Financial_Knowledge'].mean()

nota_por_rango_edad = data_2021.groupby('QD7_a')['Nota_Financial_Knowledge'].mean().sort_values(ascending=False)

nota_por_municipio = data_2021.groupby('QD1')['Nota_Financial_Knowledge'].mean().sort_values(ascending=False)

fig, axes = plt.subplots(2, 3, figsize=(25, 12))  # Cambia la disposición y el tamaño de la figura

# Gráfico 1: Nota de Conocimiento Financiero por Región
nota_por_region.plot(kind='bar', ax=axes[0,0])
axes[0,0].set_title('Nota de Conocimiento Financiero por Región')
axes[0,0].set_xlabel('Región')
axes[0,0].set_ylabel('Nota Media')
axes[0,0].set_xticklabels(['Andalucía', 'Aragón', 'Asturias', 'Baleares', 'Canarias', 'Cantabria', 'Castilla y León', 'Castilla-La Mancha', 'Cataluña', 'Valencia', 'Extremadura', 'Galicia', 'Madrid', 'Murcia', 'Navarra', 'País Vasco', 'La Rioja'], rotation=60)

# Gráfico 2: Nota de Conocimiento Financiero por Nivel de Ingresos
nota_por_ingresos.plot(kind='bar', ax=axes[0,1])
axes[0,1].set_title('Nota de Conocimiento Financiero por Nivel de Ingresos')
axes[0,1].set_xlabel('Nivel de Ingresos')
axes[0,1].set_ylabel('Nota Media')

#grafico 3: Nota de Conocimiento Financiero por rango de edad
nota_por_rango_edad.plot(kind='bar', ax=axes[0,2])
axes[0,2].set_title('Nota de Conocimiento Financiero por rango de edad')
axes[0,2].set_xlabel('rango de edad')
axes[0,2].set_ylabel('Nota Media')

# Gráfico 4: Nota de Conocimiento Financiero por Nivel de Educación
nota_por_educacion.plot(kind='bar', ax=axes[1,0])
axes[1,0].set_title('Nota de Conocimiento Financiero por Nivel de Educación')
axes[1,0].set_xlabel('Nivel de Educación ISCED')
axes[1,0].set_ylabel('Nota Media')

# Gráfico 5: Nota de Conocimiento Financiero por Tamaño de Municipio
nota_por_puesto_trabajo.plot(kind='bar', ax=axes[1,1])
axes[1,1].set_title('Nota de Conocimiento Financiero por puesto de trabajo')
axes[1,1].set_xlabel('puesto de trabajo')
axes[1,1].set_ylabel('Nota Media')
axes[1,1].set_xticklabels(['self employed', 'in paid employment', 'looking after home', 'looking for work', 'retired', 'unable to work due to sickness', 'not working and not looking for it', 'student', 'other'])

# Grafico 6: Nota de Conocimiento Financiero por tamaño de municipio 
nota_por_municipio.plot(kind='bar', ax=axes[1,2])
axes[1,2].set_title('Nota de Conocimiento Financiero por genero')
axes[1,2].set_xlabel('tamaño de municipio')
axes[1,2].set_ylabel('Nota Media')

# Añadir anotaciones a todos los ejes
for i in range(2):
    for j in range(3):
        ax = axes[i,j]
        for p in ax.patches:
            ax.annotate(f'{p.get_height():.1f}', (p.get_x() + p.get_width() / 2., p.get_height()), 
                        ha='center', va='center', xytext=(0, 10), textcoords='offset points')

  # Ajustar automáticamente los parámetros de la subtrama para dar espacio al contenido
plt.tight_layout()
plt.show()

#ver correlacion entre nota_financial_knowledge y las variables demograficas
correlacion = data_2021[['Nota_Financial_Knowledge', 'QD9_ISCED', 'QD7', 'QD2', 'QK1']].corr()

# Visualizar la matriz de correlación
plt.figure(figsize=(10, 6))
sns.heatmap(correlacion, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlación entre Nota de Conocimiento Financiero y Variables Demográficas')
plt.show()

# Definir las respuestas correctas y las múltiples respuestas correctas donde sea necesario
respuestas_correctas = {
    'QK4': 0,  # Asumiendo que '0' es la respuesta correcta para QK4 (sin interés)
    'QK5': 102, # Asumiendo que '102' es la respuesta correcta para QK5 (100 principal + 2% de interés)
    'QK7_1': True,  # Asumiendo que True es la respuesta correcta para QK7_1
    'QK7_2': True,
    'QK7_3': True
}

# Respuestas múltiples para QK3
data_encoded_2021['QK3_puntos'] = data_encoded_2021['QK3'].apply(lambda x: 1 if x in [3, 4] else 0)

# Calcular puntos por cada respuesta correcta, excepto QK6
for pregunta, correcta in respuestas_correctas.items():
    data_encoded_2021[pregunta + '_puntos'] = (data_encoded_2021[pregunta] == correcta).astype(int)

# Asignar puntos para QK6 condicionado a QK5 siendo correcto también
data_encoded_2021['QK6_puntos'] = ((data_encoded_2021['QK6'] == 1) & (data_encoded_2021['QK5'] == 102)).astype(int)

# Sumar puntos para obtener la nota final de conocimiento financiero
columnas_puntos = [pregunta + '_puntos' for pregunta in respuestas_correctas] + ['QK3_puntos', 'QK6_puntos']
data_encoded_2021['Nota_Financial_Knowledge'] = data_encoded_2021[columnas_puntos].sum(axis=1)

# Visualizar el resultado
data_encoded_2021[['Nota_Financial_Knowledge'] + columnas_puntos].head()

# eliminar columnas de puntos de data_encoded_2021
data_encoded_2021 = data_encoded_2021.drop(columns=columnas_puntos)

#eliminar columan qd3
data_encoded_2021 = data_encoded_2021.drop(columns='QD3')

from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Determinar el número óptimo de clusters usando el método del codo
sse = []
k_range = range(1, 15)
for k in k_range:
    kmeans = KMeans(n_clusters=k, random_state=42)
    kmeans.fit(data_encoded_2021)
    sse.append(kmeans.inertia_)

# Graficar el método del codo
plt.figure(figsize=(10, 6))
plt.plot(k_range, sse, marker='o')
plt.xlabel('Número de Clusters')
plt.ylabel('SSE')
plt.title('Método del Codo para Determinar el Número de Clusters')
plt.show()

data_numeric = data_encoded_2021.select_dtypes(include=['int', 'float'])

# Escalar los datos
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data_numeric)  # Escalar los datos numéricos

# # Aplicar PCA
# pca = PCA(n_components=0.95)  # Conservar el 95% de la varianza
# data_reduced = pca.fit_transform(data_scaled)

# Aplicar K-Means
kmeans = KMeans(n_clusters=4, random_state=42)
clusters = kmeans.fit_predict(data_scaled)

# Añadir los clusters al dataframe original
data_encoded_2021['Cluster'] = clusters

# Comparar edad entre clusters
plt.figure(figsize=(10, 6))
sns.boxplot(x='Cluster', y='QD7', data=data_encoded_2021)
plt.title('Distribución de Edad por Cluster')
plt.show()

# Comparar ingresos entre clusters
plt.figure(figsize=(10, 6))
sns.countplot(x='QD2', hue='Cluster', data=data_encoded_2021)
plt.title('Distribución de region por Cluster')
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(x='Cluster', y='Nota_Financial_Knowledge', data=data_encoded_2021)
plt.title('Nota de Conocimiento Financiero por Cluster')
plt.show()

# Por ejemplo, para el Cluster 0:
cluster_0 = data_encoded_2021[data_encoded_2021['Cluster'] == 0]
print(cluster_0.describe())

# Distribución de edades en el Cluster 0
plt.figure(figsize=(10, 5))
sns.histplot(cluster_0['QD7'], bins=20)
plt.title('Distribución de Edad en el Cluster 0')
plt.xlabel('Edad')
plt.ylabel('Frecuencia')
plt.show()


# Nivel de educación en el Cluster 0
plt.figure(figsize=(10,5))
sns.countplot(x='QD9_ISCED', data=cluster_0)
plt.title('Nivel de Educación en el Cluster 0')
plt.xlabel('Nivel de Educación ISCED')
plt.ylabel('Frecuencia')
plt.show()

# Nota de conocimiento financiero en el Cluster 0
plt.figure(figsize=(10,5))
sns.histplot(cluster_0['Nota_Financial_Knowledge'], bins=20)
plt.title('Nota de Conocimiento Financiero en el Cluster 0')
plt.xlabel('Nota de Conocimiento Financiero')
plt.ylabel('Frecuencia')
plt.show()

               wght          QD1          QD2        QD5_1        QD5_2  \
count   3267.000000  3267.000000  3267.000000  3267.000000  3267.000000   
mean    4624.480430     0.499541     8.827977     0.117845     0.605448   
std     3046.917124     0.500076     4.821725     0.322474     0.488829   
min      432.794220     0.000000     1.000000     0.000000     0.000000   
25%     2430.563600     0.000000     5.000000     0.000000     0.000000   
50%     3820.336400     0.000000     9.000000     0.000000     1.000000   
75%     6282.485600     1.000000    13.000000     0.000000     1.000000   
max    19411.416000     1.000000    17.000000     1.000000     1.000000   

             QD5_3        QD5_4        QD5_5        QD5_6        QD5_7  ...  \
count  3267.000000  3267.000000  3267.000000  3267.000000  3267.000000  ...   
mean      0.213958     0.202326     0.265687     0.002143     0.003061  ...   
std       0.410160     0.401796     0.441766     0.046246     0.055249  ...   
min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   
25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   
50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   
75%       0.000000     0.000000     1.000000     0.000000     0.000000  ...   
max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   

       Product_Crypto-assets  Product_Dont_know  Product_Error  \
count                 3267.0        3267.000000    3267.000000   
mean                     0.0           0.001224       0.000306   
std                      0.0           0.034975       0.017495   
min                      0.0           0.000000       0.000000   
25%                      0.0           0.000000       0.000000   
50%                      0.0           0.000000       0.000000   
75%                      0.0           0.000000       0.000000   
max                      0.0           1.000000       1.000000   

       Product_Insurance  Product_Not_applicable  \
count        3267.000000             3267.000000   
mean            0.000612                0.990511   
std             0.024739                0.096962   
min             0.000000                0.000000   
25%             0.000000                1.000000   
50%             0.000000                1.000000   
75%             0.000000                1.000000   
max             1.000000                1.000000   

       Product_Not_voluntarily_chosen  Product_Refused_to_answer  \
count                     3267.000000                3267.000000   
mean                         0.006428                   0.000306   
std                          0.079928                   0.017495   
min                          0.000000                   0.000000   
25%                          0.000000                   0.000000   
50%                          0.000000                   0.000000   
75%                          0.000000                   0.000000   
max                          1.000000                   1.000000   

       Product_Stocks and shares  Nota_Financial_Knowledge  Cluster  
count                3267.000000               3267.000000   3267.0  
mean                    0.000306                  4.523416      0.0  
std                     0.017495                  1.494321      0.0  
min                     0.000000                  0.000000      0.0  
25%                     0.000000                  3.000000      0.0  
50%                     0.000000                  4.000000      0.0  
75%                     0.000000                  6.000000      0.0  
max                     1.000000                  7.000000      0.0  

[8 rows x 170 columns]

#eliminar wght de data_encoded_2021
data_encoded_2021.drop('wght', axis=1, inplace=True)

from sklearn.preprocessing import StandardScaler

# Normalizar las variables numéricas
scaler = StandardScaler()
data_normalized = scaler.fit_transform(data_encoded_2021)

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# Aplicar PCA
pca = PCA(n_components=2)  # Reducimos a 2 componentes principales para visualización
data_pca = pca.fit_transform(data_encoded_2021)

# Visualización de los componentes principales
plt.figure(figsize=(10, 6))
plt.scatter(data_pca[:, 0], data_pca[:, 1])
plt.xlabel('Componente Principal 1')
plt.ylabel('Componente Principal 2')
plt.title('PCA: Visualización de Componentes Principales')
plt.show()

# Explicación de la varianza
print("Varianza explicada por cada componente principal:", pca.explained_variance_ratio_)

Varianza explicada por cada componente principal: [0.41837088 0.25503746]

# Ver las contribuciones de las variables originales a los componentes principales
pca_components = pd.DataFrame(pca.components_, columns=data_encoded_2021.columns)
#print(pca_components)

# Identificar las variables con las mayores contribuciones a PC1 y PC2
print(pca_components.iloc[0].sort_values(ascending=False).head(10))  # Para PC1
print(pca_components.iloc[1].sort_values(ascending=False).head(10))  # Para PC2

QF12_3_1    0.225902
QF12_6_2    0.225890
QF12_7_1    0.225890
QF12_3_2    0.225885
QF12_3_6    0.225883
QF12_3_3    0.225882
QF12_5_3    0.225882
QF12_3_7    0.225882
QF12_1_3    0.225877
QF12_4_1    0.225876
Name: 0, dtype: float64
QP7_98                    0.170411
QF9_97                    0.149936
Product_Not_applicable    0.124910
QD9                       0.101324
Income_Below 15000€       0.070012
QF12_98                   0.056251
QP3_98                    0.054912
QD10                      0.053547
QF9_99                    0.051622
Income_Do not know        0.032567
Name: 1, dtype: float64

# eliminar variables QK1 a QK7_3
data_encoded_2021.drop(['QK1', 'QK2', 'QK3', 'QK4', 'QK5', 'QK6', 'QK7_1', 'QK7_2', 'QK7_3', 'QD9'], axis=1, inplace=True)

from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt

# Definir la variable dependiente y las variables independientes
X = data_encoded_2021.drop(columns=['Nota_Financial_Knowledge'])
y = data_encoded_2021['Nota_Financial_Knowledge']

# Crear y ajustar el modelo de árbol de decisión
tree_model = DecisionTreeRegressor(random_state=42)
tree_model.fit(X, y)

# Obtener la importancia de las variables
importance = tree_model.feature_importances_

# Crear un dataframe para mostrar la importancia de las variables
features = pd.DataFrame({'Feature': X.columns, 'Importance': importance})
features = features.sort_values(by='Importance', ascending=False)

# Visualizar la importancia de las variables
plt.figure(figsize=(10, 30))
plt.barh(features['Feature'], features['Importance'])
plt.xlabel('Importancia')
plt.ylabel('Variable')
plt.title('Importancia de las Variables')
plt.gca().invert_yaxis()
plt.show()

# Importar las libreria necesarias para el modelo de regresión ordinal
import statsmodels.api as sm
from statsmodels.miscmodels.ordinal_model import OrderedModel

# Definir un umbral de importancia
importance_threshold = 0.01

# Filtrar las variables que superan el umbral de importancia
relevant_features = features[features['Importance'] > importance_threshold]['Feature']
print("Variables relevantes:", relevant_features)

# Crear un nuevo DataFrame solo con las variables relevantes
X_relevant = X[relevant_features]

# Ajustar nuevamente el modelo de regresión ordinal con las variables relevantes
model_relevant = OrderedModel(y, X_relevant, distr='logit')
result_relevant = model_relevant.fit(method='bfgs')

# Mostrar el resumen del nuevo modelo
result_relevant.summary()

Variables relevantes: 122    QD9_ISCED
0            QD1
121          QD7
93        QP3_16
1            QD2
109        QS1_2
92        QP3_13
110        QS1_3
113        QS1_7
25           QF8
111        QS1_4
114        QS1_8
78        QP2_12
123         QD10
119        QS3_9
112        QS1_5
120       QS3_11
79        QP2_13
57          QF13
117        QS2_3
115       QS1_10
71         QP2_2
108        QS1_1
12           QF1
10        QD5_ad
Name: Feature, dtype: object
Optimization terminated successfully.
         Current function value: 1.577848
         Iterations: 65
         Function evaluations: 70
         Gradient evaluations: 70

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix

# Preparar los datos
X = data_encoded_2021.drop(columns=['Nota_Financial_Knowledge'])
y = data_encoded_2021['Nota_Financial_Knowledge']

# Dividir los datos en conjuntos de entrenamiento y prueba
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Crear el modelo de regresión logística multinomial
logistic_model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=1000)
logistic_model.fit(X_train, y_train)

# Realizar predicciones
y_pred = logistic_model.predict(X_test)

# Evaluar el modelo
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

[[  0   0   0   4   1   0   0   1]
 [  0   1   1  10  10   4   0   0]
 [  4   2   5  37  57  15  11   2]
 [  1   1   5  63 154  54  33  10]
 [  2   1   4  68 225 102  98  20]
 [  1   0   6  32 151 116 150  63]
 [  1   0   0   8  76 101 184  76]
 [  0   0   1   3  33  45 139 138]]
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         6
           1       0.20      0.04      0.06        26
           2       0.23      0.04      0.06       133
           3       0.28      0.20      0.23       321
           4       0.32      0.43      0.37       520
           5       0.27      0.22      0.24       519
           6       0.30      0.41      0.35       446
           7       0.45      0.38      0.41       359

    accuracy                           0.31      2330
   macro avg       0.25      0.22      0.22      2330
weighted avg       0.31      0.31      0.30      2330

/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, train_test_split, StratifiedKFold
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np

# Usar una muestra del conjunto de datos para acelerar el ajuste de hiperparámetros
X_train_sample, _, y_train_sample, _ = train_test_split(X_train, y_train, train_size=0.1, random_state=42)

# Definir los parámetros a ajustar (espacio reducido)
param_grid = {
    'C': [0.1, 1, 10],
    'solver': ['newton-cg', ]  # saga soporta la regresión multinomial y es rápido
}

# Crear el modelo de regresión logística multinomial
logistic_model = LogisticRegression(multi_class='multinomial', max_iter=20000)

# Configurar GridSearchCV con StratifiedKFold y paralelización
cv = StratifiedKFold(n_splits=3)
grid_search = GridSearchCV(logistic_model, param_grid, cv=cv, scoring='accuracy', n_jobs=-1)
grid_search.fit(X_train_sample, y_train_sample)

# Mejor modelo encontrado
best_model = grid_search.best_estimator_
print("Mejores hiperparámetros:", grid_search.best_params_)

# Realizar predicciones con el mejor modelo
y_pred_best = best_model.predict(X_test)

# Evaluar el modelo
conf_matrix_best = confusion_matrix(y_test, y_pred_best)
class_report_best = classification_report(y_test, y_pred_best)

print("Matriz de Confusión del Mejor Modelo:")
print(conf_matrix_best)
print("\nInforme de Clasificación del Mejor Modelo:")
print(class_report_best)

Mejores hiperparámetros: {'C': 1, 'solver': 'newton-cg'}
Matriz de Confusión del Mejor Modelo:
[[  0   0   0   4   1   0   0   1]
 [  0   3   3   9   8   3   0   0]
 [  0   3  10  38  45  17  13   7]
 [  0  12  34  76  95  47  46  11]
 [  0  12  30  80 158 104  95  41]
 [  0   5  24  48 122 123 121  76]
 [  0   2   9  23  68 101 144  99]
 [  0   1   6  15  35  53 128 121]]

Informe de Clasificación del Mejor Modelo:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         6
           1       0.08      0.12      0.09        26
           2       0.09      0.08      0.08       133
           3       0.26      0.24      0.25       321
           4       0.30      0.30      0.30       520
           5       0.27      0.24      0.25       519
           6       0.26      0.32      0.29       446
           7       0.34      0.34      0.34       359

    accuracy                           0.27      2330
   macro avg       0.20      0.20      0.20      2330
weighted avg       0.27      0.27      0.27      2330

/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from imblearn.over_sampling import SMOTE
import pandas as pd

# Verificar el número de muestras en la clase minoritaria
class_counts = y_train.value_counts()
min_class_count = class_counts.min()

# Ajustar k_neighbors para SMOTE
k_neighbors = min(5, min_class_count - 1)

# Aplicar SMOTE para balancear las clases en el conjunto de entrenamiento
smote = SMOTE(random_state=42, k_neighbors=k_neighbors)
X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)

# Verificar el balanceo
print("Distribución de clases antes de SMOTE:")
print(y_train.value_counts())
print("\nDistribución de clases después de SMOTE:")
print(pd.Series(y_train_balanced).value_counts())

Distribución de clases antes de SMOTE:
Nota_Financial_Knowledge
4    1237
5    1172
6    1064
3     833
7     775
2     283
1      66
0       4
Name: count, dtype: int64

Distribución de clases después de SMOTE:
Nota_Financial_Knowledge
4    1237
5    1237
2    1237
3    1237
7    1237
6    1237
1    1237
0    1237
Name: count, dtype: int64

# Crear el modelo de Random Forest
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train_balanced, y_train_balanced)

RandomForestClassifier(random_state=42)

RandomForestClassifier(random_state=42)

# Realizar predicciones
y_pred_rf = rf_model.predict(X_test)

# Evaluar el modelo
conf_matrix_rf = confusion_matrix(y_test, y_pred_rf)
class_report_rf = classification_report(y_test, y_pred_rf)

print("Matriz de Confusión del Modelo Random Forest:")
print(conf_matrix_rf)
print("\nInforme de Clasificación del Modelo Random Forest:")
print(class_report_rf)

Matriz de Confusión del Modelo Random Forest:
[[  0   2   1   1   1   1   0   0]
 [  0   1   4  10   8   3   0   0]
 [  0   5  18  38  46  17   7   2]
 [  0   8  19 104  93  54  33  10]
 [  0   1  30 115 150 103  80  41]
 [  0   0  19  80 107  97 129  87]
 [  0   0   4  25  51  74 169 123]
 [  0   0   1  10  32  30 113 173]]

Informe de Clasificación del Modelo Random Forest:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         6
           1       0.06      0.04      0.05        26
           2       0.19      0.14      0.16       133
           3       0.27      0.32      0.30       321
           4       0.31      0.29      0.30       520
           5       0.26      0.19      0.22       519
           6       0.32      0.38      0.35       446
           7       0.40      0.48      0.44       359

    accuracy                           0.31      2330
   macro avg       0.22      0.23      0.22      2330
weighted avg       0.30      0.31      0.30      2330

/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

import matplotlib.pyplot as plt
import seaborn as sns

# Obtener la importancia de las características
importances = rf_model.feature_importances_
feature_names = X.columns
feature_importance = pd.DataFrame({'Feature': feature_names, 'Importance': importances})
feature_importance = feature_importance.sort_values(by='Importance', ascending=False)

# Visualizar la importancia de las características
plt.figure(figsize=(12, 30))
sns.barplot(x='Importance', y='Feature', data=feature_importance)
plt.title('Importancia de las Variables en el Modelo Random Forest')
plt.show()

from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
import pandas as pd

# Supongamos que data_encoded es tu DataFrame con las variables codificadas y 'Nota_Financial_Knowledge' es la variable dependiente

# Preparar los datos
X = data_encoded_2021.drop(columns=['Nota_Financial_Knowledge'])
y = data_encoded_2021['Nota_Financial_Knowledge']

# Dividir los datos en conjuntos de entrenamiento y prueba
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Ajustar k_neighbors para SMOTE
class_counts = y_train.value_counts()
min_class_count = class_counts.min()
k_neighbors = min(5, min_class_count - 1)

# Aplicar SMOTE para balancear las clases en el conjunto de entrenamiento
smote = SMOTE(random_state=42, k_neighbors=k_neighbors)
X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)

# Verificar el balanceo
print("Distribución de clases antes de SMOTE:")
print(y_train.value_counts())
print("\nDistribución de clases después de SMOTE:")
print(pd.Series(y_train_balanced).value_counts())

Distribución de clases antes de SMOTE:
Nota_Financial_Knowledge
4    1237
5    1172
6    1064
3     833
7     775
2     283
1      66
0       4
Name: count, dtype: int64

Distribución de clases después de SMOTE:
Nota_Financial_Knowledge
4    1237
5    1237
2    1237
3    1237
7    1237
6    1237
1    1237
0    1237
Name: count, dtype: int64

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import classification_report, confusion_matrix

# Crear el modelo de Gradient Boosting
gb_model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, random_state=42)
gb_model.fit(X_train, y_train)

GradientBoostingClassifier(random_state=42)

GradientBoostingClassifier(random_state=42)

# Realizar predicciones
y_pred_gb = gb_model.predict(X_test)

# Evaluar el modelo
conf_matrix_gb = confusion_matrix(y_test, y_pred_gb)
class_report_gb = classification_report(y_test, y_pred_gb)

print("Matriz de Confusión del Modelo Gradient Boosting:")
print(conf_matrix_gb)
print("\nInforme de Clasificación del Modelo Gradient Boosting:")
print(class_report_gb)

Matriz de Confusión del Modelo Gradient Boosting:
[[  0   0   0   3   3   0   0   0]
 [  0   2   0   8  12   4   0   0]
 [  0   2   7  52  48  12  11   1]
 [  0   2   9  80 143  48  32   7]
 [  0   1   4  97 209 113  82  14]
 [  0   3   7  53 138 118 148  52]
 [  0   0   1  10  84  90 177  84]
 [  0   1   1   4  31  53 120 149]]

Informe de Clasificación del Modelo Gradient Boosting:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         6
           1       0.18      0.08      0.11        26
           2       0.24      0.05      0.09       133
           3       0.26      0.25      0.25       321
           4       0.31      0.40      0.35       520
           5       0.27      0.23      0.25       519
           6       0.31      0.40      0.35       446
           7       0.49      0.42      0.45       359

    accuracy                           0.32      2330
   macro avg       0.26      0.23      0.23      2330
weighted avg       0.32      0.32      0.31      2330

/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

import xgboost as xgb
from sklearn.metrics import classification_report, confusion_matrix

# Preparar los datos para XGBoost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

# Parámetros de XGBoost
params = {
    'objective': 'multi:softmax',
    'num_class': len(set(y)),  # Número de clases
    'max_depth': 6,
    'eta': 0.1,
    'seed': 42
}

# Entrenar el modelo
xgb_model = xgb.train(params, dtrain, num_boost_round=100)

# Realizar predicciones
y_pred_xgb = xgb_model.predict(dtest)

# Evaluar el modelo
conf_matrix_xgb = confusion_matrix(y_test, y_pred_xgb)
class_report_xgb = classification_report(y_test, y_pred_xgb)

print("Matriz de Confusión del Modelo XGBoost:")
print(conf_matrix_xgb)
print("\nInforme de Clasificación del Modelo XGBoost:")
print(class_report_xgb)

Matriz de Confusión del Modelo XGBoost:
[[  0   1   0   4   1   0   0   0]
 [  0   0   0  13  11   2   0   0]
 [  0   2   5  45  52  14  13   2]
 [  0   2   6  76 145  54  33   5]
 [  0   1   6  92 205 106  91  19]
 [  0   0   2  57 140 123 131  66]
 [  0   0   0   6  92  80 170  98]
 [  0   0   0   4  28  59 126 142]]

Informe de Clasificación del Modelo XGBoost:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         6
           1       0.00      0.00      0.00        26
           2       0.26      0.04      0.07       133
           3       0.26      0.24      0.25       321
           4       0.30      0.39      0.34       520
           5       0.28      0.24      0.26       519
           6       0.30      0.38      0.34       446
           7       0.43      0.40      0.41       359

    accuracy                           0.31      2330
   macro avg       0.23      0.21      0.21      2330
weighted avg       0.30      0.31      0.30      2330

/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/workspaces/TFM/.conda/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

# Importar las librerias necesarias
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import classification_report, confusion_matrix

# Suponiendo que data_encoded es tu DataFrame con las variables codificadas y 'Nota_Financial_Knowledge' es la variable dependiente

# Preparar los datos
X = data_encoded_2021.drop(columns=['Nota_Financial_Knowledge'])
y = data_encoded_2021['Nota_Financial_Knowledge']

# Dividir los datos en conjuntos de entrenamiento y prueba
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Escalar los datos
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Codificar las etiquetas en formato one-hot
encoder = OneHotEncoder(sparse_output=False)
y_train_encoded = encoder.fit_transform(y_train.values.reshape(-1, 1))
y_test_encoded = encoder.transform(y_test.values.reshape(-1, 1))

# Definir la arquitectura de la red neuronal
model = Sequential()

# Capa de entrada
model.add(Dense(128, input_dim=X_train_scaled.shape[1], activation='relu'))

# Capas ocultas
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))  # Dropout para reducir el overfitting

model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))  # Dropout para reducir el overfitting

# Capa de salida
model.add(Dense(len(encoder.categories_[0]), activation='softmax'))

# Compilar el modelo
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

/workspaces/TFM/.conda/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

# Entrenar el modelo
history = model.fit(X_train_scaled, y_train_encoded, epochs=50, batch_size=32, validation_data=(X_test_scaled, y_test_encoded))

Epoch 1/50

170/170 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.1611 - loss: 2.1815 - val_accuracy: 0.2850 - val_loss: 1.7193
Epoch 2/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.2574 - loss: 1.7763 - val_accuracy: 0.2931 - val_loss: 1.6440
Epoch 3/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.2711 - loss: 1.6866 - val_accuracy: 0.3052 - val_loss: 1.6311
Epoch 4/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.3083 - loss: 1.6297 - val_accuracy: 0.3099 - val_loss: 1.6127
Epoch 5/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3000 - loss: 1.6183 - val_accuracy: 0.3223 - val_loss: 1.6079
Epoch 6/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3211 - loss: 1.5917 - val_accuracy: 0.3034 - val_loss: 1.6225
Epoch 7/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3215 - loss: 1.5607 - val_accuracy: 0.3163 - val_loss: 1.6145
Epoch 8/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3272 - loss: 1.5727 - val_accuracy: 0.3103 - val_loss: 1.6186
Epoch 9/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3235 - loss: 1.5448 - val_accuracy: 0.3193 - val_loss: 1.6172
Epoch 10/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3378 - loss: 1.5329 - val_accuracy: 0.3064 - val_loss: 1.6287
Epoch 11/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3438 - loss: 1.5246 - val_accuracy: 0.3047 - val_loss: 1.6278
Epoch 12/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3371 - loss: 1.5300 - val_accuracy: 0.3026 - val_loss: 1.6352
Epoch 13/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3399 - loss: 1.5211 - val_accuracy: 0.3112 - val_loss: 1.6326
Epoch 14/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3467 - loss: 1.5116 - val_accuracy: 0.3073 - val_loss: 1.6340
Epoch 15/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3457 - loss: 1.4991 - val_accuracy: 0.3150 - val_loss: 1.6518
Epoch 16/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3550 - loss: 1.4771 - val_accuracy: 0.3107 - val_loss: 1.6405
Epoch 17/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3640 - loss: 1.4794 - val_accuracy: 0.3004 - val_loss: 1.6596
Epoch 18/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3642 - loss: 1.4743 - val_accuracy: 0.3120 - val_loss: 1.6846
Epoch 19/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3538 - loss: 1.4664 - val_accuracy: 0.2961 - val_loss: 1.6948
Epoch 20/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3692 - loss: 1.4479 - val_accuracy: 0.2974 - val_loss: 1.6933
Epoch 21/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3612 - loss: 1.4405 - val_accuracy: 0.3052 - val_loss: 1.6981
Epoch 22/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3722 - loss: 1.4279 - val_accuracy: 0.2845 - val_loss: 1.7076
Epoch 23/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3749 - loss: 1.4321 - val_accuracy: 0.2966 - val_loss: 1.7117
Epoch 24/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3724 - loss: 1.4182 - val_accuracy: 0.2987 - val_loss: 1.7300
Epoch 25/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3821 - loss: 1.4121 - val_accuracy: 0.3043 - val_loss: 1.7472
Epoch 26/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3874 - loss: 1.3947 - val_accuracy: 0.2828 - val_loss: 1.7378
Epoch 27/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3780 - loss: 1.4052 - val_accuracy: 0.2871 - val_loss: 1.7780
Epoch 28/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3651 - loss: 1.4157 - val_accuracy: 0.2918 - val_loss: 1.8060
Epoch 29/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3880 - loss: 1.3776 - val_accuracy: 0.3013 - val_loss: 1.7783
Epoch 30/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3889 - loss: 1.3703 - val_accuracy: 0.2979 - val_loss: 1.7827
Epoch 31/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4031 - loss: 1.3627 - val_accuracy: 0.2897 - val_loss: 1.8407
Epoch 32/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3868 - loss: 1.3774 - val_accuracy: 0.2923 - val_loss: 1.8641
Epoch 33/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3966 - loss: 1.3574 - val_accuracy: 0.2833 - val_loss: 1.8933
Epoch 34/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4035 - loss: 1.3512 - val_accuracy: 0.2863 - val_loss: 1.9011
Epoch 35/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4128 - loss: 1.3378 - val_accuracy: 0.2880 - val_loss: 1.9666
Epoch 36/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.4372 - loss: 1.3167 - val_accuracy: 0.2918 - val_loss: 2.0194
Epoch 37/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4298 - loss: 1.3233 - val_accuracy: 0.2820 - val_loss: 1.9680
Epoch 38/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4270 - loss: 1.3143 - val_accuracy: 0.2880 - val_loss: 1.9863
Epoch 39/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4150 - loss: 1.3290 - val_accuracy: 0.2833 - val_loss: 2.0480
Epoch 40/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4342 - loss: 1.2871 - val_accuracy: 0.2794 - val_loss: 2.0627
Epoch 41/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4175 - loss: 1.2974 - val_accuracy: 0.2867 - val_loss: 2.1331
Epoch 42/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4248 - loss: 1.2847 - val_accuracy: 0.2760 - val_loss: 2.1523
Epoch 43/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4340 - loss: 1.2974 - val_accuracy: 0.2824 - val_loss: 2.1376
Epoch 44/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4433 - loss: 1.2649 - val_accuracy: 0.2815 - val_loss: 2.2964
Epoch 45/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.4455 - loss: 1.2590 - val_accuracy: 0.2790 - val_loss: 2.1692
Epoch 46/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4420 - loss: 1.2673 - val_accuracy: 0.2773 - val_loss: 2.2392
Epoch 47/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4487 - loss: 1.2620 - val_accuracy: 0.2803 - val_loss: 2.4333
Epoch 48/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4415 - loss: 1.2660 - val_accuracy: 0.2837 - val_loss: 2.3534
Epoch 49/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4685 - loss: 1.2076 - val_accuracy: 0.2781 - val_loss: 2.4696
Epoch 50/50
170/170 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.4658 - loss: 1.2329 - val_accuracy: 0.2712 - val_loss: 2.3706

# Evaluar el modelo en el conjunto de prueba
loss, accuracy = model.evaluate(X_test_scaled, y_test_encoded)
print(f'Precisión del modelo en el conjunto de prueba: {accuracy * 100:.2f}%')

# Realizar predicciones
y_pred_prob = model.predict(X_test_scaled)
y_pred = np.argmax(y_pred_prob, axis=1)

# Convertir las etiquetas one-hot a etiquetas originales
y_test_original = np.argmax(y_test_encoded, axis=1)

# Evaluar el modelo
conf_matrix_nn = confusion_matrix(y_test_original, y_pred)
class_report_nn = classification_report(y_test_original, y_pred)

print("Matriz de Confusión del Modelo de Red Neuronal:")
print(conf_matrix_nn)
print("\nInforme de Clasificación del Modelo de Red Neuronal:")
print(class_report_nn)

73/73 ━━━━━━━━━━━━━━━━━━━━ 0s 637us/step - accuracy: 0.2734 - loss: 2.2322
Precisión del modelo en el conjunto de prueba: 27.12%
73/73 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step

73/73 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
Matriz de Confusión del Modelo de Red Neuronal:
[[  0   0   1   2   1   0   0   2]
 [  0   0   1  12   7   6   0   0]
 [  0   0   7  59  34  23   8   2]
 [  1   2  12 106  97  74  24   5]
 [  0   0   7 145 143 128  69  28]
 [  0   0   4  85 116 128 132  54]
 [  0   1   5  44  59 115 138  84]
 [  0   0   0  16  31  72 130 110]]

Informe de Clasificación del Modelo de Red Neuronal:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         6
           1       0.00      0.00      0.00        26
           2       0.19      0.05      0.08       133
           3       0.23      0.33      0.27       321
           4       0.29      0.28      0.28       520
           5       0.23      0.25      0.24       519
           6       0.28      0.31      0.29       446
           7       0.39      0.31      0.34       359

    accuracy                           0.27      2330
   macro avg       0.20      0.19      0.19      2330
weighted avg       0.27      0.27      0.27      2330

	Missing Values	%
QF3_4	7764	100.0
QP7_2	7764	100.0
QP2_17	7764	100.0
QP3_4	7764	100.0
QP3_6	7764	100.0
QP3_8	7764	100.0
QP3_10	7764	100.0
QP3_14	7764	100.0
QP3_15	7764	100.0
QP3_17	7764	100.0
QP7_6	7764	100.0
QP2_14	7764	100.0
QP10_1	7764	100.0
QP10_2	7764	100.0
QP10_4	7764	100.0
QP10_5	7764	100.0
QP10_6	7764	100.0
QP10_7	7764	100.0
QP10_8	7764	100.0
QP10_9	7764	100.0
QP2_15	7764	100.0
QP2_10	7764	100.0
QF3_5	7764	100.0
QF12_5_4	7764	100.0
QF9_10	7764	100.0
QF9_11	7764	100.0
QF12_1_2	7764	100.0
QF12_2_1	7764	100.0
QF12_2_3	7764	100.0
QF12_3_4	7764	100.0
QF12_3_5	7764	100.0
QF12_5_2	7764	100.0
QF12_5_5	7764	100.0
QP2_6	7764	100.0
QP1_4	7764	100.0
QP1_6	7764	100.0
QP1_8	7764	100.0
QP1_10	7764	100.0
QP1_14	7764	100.0
QP1_15	7764	100.0
QP1_17	7764	100.0
QP2_4	7764	100.0
QP10_10	7764	100.0

	Nota_Financial_Knowledge	QK4_puntos	QK5_puntos	QK7_1_puntos	QK7_2_puntos	QK7_3_puntos	QK3_puntos	QK6_puntos
ID
1	5	1	1	1	1	1	0	0
2	6	1	1	0	1	1	1	1
3	5	1	1	1	1	1	0	0
4	5	1	1	1	1	1	0	0
5	5	1	1	1	0	1	0	1

	Nota_Financial_Knowledge	QK4_puntos	QK5_puntos	QK7_1_puntos	QK7_2_puntos	QK7_3_puntos	QK3_puntos	QK6_puntos
ID
1	5	1	1	1	1	1	0	0
2	6	1	1	0	1	1	1	1
3	5	1	1	1	1	1	0	0
4	5	1	1	1	1	1	0	0
5	5	1	1	1	0	1	0	1

Dep. Variable:	Nota_Financial_Knowledge	Log-Likelihood:	-12250.
Model:	OrderedModel	AIC:	2.456e+04
Method:	Maximum Likelihood	BIC:	2.479e+04
Date:	Thu, 16 May 2024
Time:	11:41:48
No. Observations:	7764
Df Residuals:	7732
Df Model:	25

	coef	std err	z	P>\|z\|	[0.025	0.975]
QD9_ISCED	0.3684	0.013	28.732	0.000	0.343	0.394
QD1	1.0341	0.043	23.996	0.000	0.950	1.119
QD7	0.0097	0.002	6.419	0.000	0.007	0.013
QP3_16	0.0068	0.001	9.301	0.000	0.005	0.008
QD2	0.0176	0.004	4.115	0.000	0.009	0.026
QS1_2	0.0062	0.002	3.259	0.001	0.002	0.010
QP3_13	-0.0058	0.006	-0.939	0.348	-0.018	0.006
QS1_3	-0.0044	0.005	-0.876	0.381	-0.014	0.005
QS1_7	0.0077	0.003	2.574	0.010	0.002	0.014
QF8	0.0065	0.004	1.655	0.098	-0.001	0.014
QS1_4	-0.0014	0.006	-0.244	0.807	-0.013	0.010
QS1_8	0.0044	0.002	1.848	0.065	-0.000	0.009
QP2_12	0.0044	0.001	4.869	0.000	0.003	0.006
QD10	-0.0083	0.010	-0.871	0.384	-0.027	0.010
QS3_9	0.0108	0.006	1.819	0.069	-0.001	0.022
QS1_5	0.0019	0.004	0.504	0.614	-0.005	0.009
QS3_11	0.0165	0.004	3.749	0.000	0.008	0.025
QP2_13	0.0126	0.006	2.064	0.039	0.001	0.025
QF13	0.0034	0.001	2.433	0.015	0.001	0.006
QS2_3	-0.0061	0.006	-0.946	0.344	-0.019	0.007
QS1_10	0.0026	0.005	0.468	0.640	-0.008	0.013
QP2_2	0.0029	0.001	3.469	0.001	0.001	0.004
QS1_1	0.0058	0.004	1.640	0.101	-0.001	0.013
QF1	-0.1851	0.039	-4.747	0.000	-0.262	-0.109
QD5_ad	0.0022	0.001	3.101	0.002	0.001	0.004
0/1	-5.7020	0.357	-15.969	0.000	-6.402	-5.002
1/2	0.9241	0.125	7.370	0.000	0.678	1.170
2/3	0.6248	0.052	12.072	0.000	0.523	0.726
3/4	0.4726	0.028	16.659	0.000	0.417	0.528
4/5	0.3025	0.022	13.759	0.000	0.259	0.346
5/6	0.1519	0.022	6.809	0.000	0.108	0.196
6/7	0.2958	0.024	12.487	0.000	0.249	0.342

TRABAJO DE FIN DE MÁSTER:¶

Análisis de la educación financiera de los hogares españoles¶

data_2021 Cleaning y descripción¶

Decription¶

Distribución de las edades¶

Missing values¶

Columnas tipo object (encoding?)¶

deteccion de outliers¶

Correlación entre variables de interes¶

BARPLOTS VARIABLES¶

financial knowledge (QK)¶

nota financial knowledge¶

cluster analysis¶

PCA¶

arbol de decision para sacar variables relevantes y despues hacer una regresion ordinal¶

Regresion logistica¶

random forest¶

Gradient boosting¶

XGBOOST¶

RED NEURONAL¶

	ID	wght	QD1	QD2	QD3	QD4	QD5_1	QD5_2	QD5_3	QD5_4	...	QD11_4	QD11_5	QD11_6	QD11_7	QD11_8	QD11_9	QD11_10	QD11_99	QD12	QD13
0	1	2594.2485	0	8	if equal or above 15000	Spanish	0	0	0	1	...	0	0	0	0	0	0	0	0	1	Between 15000 and 47000 euro
1	2	1825.7745	1	11	if below 15000	Spanish	0	1	1	0	...	0	0	0	0	0	0	0	0	1	Between 15000 and 47000 euro
2	3	3207.1128	1	14	if equal or above 15000	Spanish	0	1	1	0	...	0	0	0	0	0	0	0	0	0	Below 15000 euro -bottom 25
3	4	3009.3894	0	8	if below 15000	Spanish	0	1	1	0	...	0	0	0	0	0	0	0	0	1	Above 47000 euro -top 25
4	5	5779.9355	1	9	if equal or above 15000	Spanish	0	1	0	1	...	0	0	0	0	0	0	0	0	0	Between 15000 and 47000 euro
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7759	7760	4408.7930	1	4	if below 15000	Spanish	0	1	0	1	...	0	0	0	0	0	0	0	0	1	Between 15000 and 47000 euro
7760	7761	11130.0900	0	10	if equal or above 15000	Spanish	0	1	0	0	...	1	0	0	0	0	0	0	0	1	Between 15000 and 47000 euro
7761	7762	1201.2681	1	11	if equal or above 15000	Spanish	0	0	0	0	...	0	0	0	0	0	0	0	0	1	Do not know
7762	7763	7075.0142	0	12	if equal or above 15000	Spanish	0	1	0	0	...	0	0	0	0	0	0	0	0	1	Refuse to answer
7763	7764	5978.3618	0	12	if below 15000	Spanish	0	1	0	0	...	0	0	0	0	0	0	0	0	1	Below 15000 euro -bottom 25